Can MARL optimize material handling throughput?
MULTI-AGENT REINFORCEMENT LEARNING FOR DYNAMIC DISPATCHING IN MATERIAL HANDLING SYSTEMS
October 1, 2024
https://arxiv.org/pdf/2409.18435This paper proposes using Multi-Agent Reinforcement Learning (MARL) to optimize dynamic dispatching in material handling systems, like deciding where to send packages in a warehouse.
The key takeaway for LLM-based multi-agent systems is the use of existing heuristics (rule-based systems) to guide the MARL training process, improving exploration and leading to better performance than either heuristics or MARL alone. This highlights the potential of combining LLMs' knowledge encoding with RL's adaptability in multi-agent scenarios.